Pattern　recognition　models　trained　on　low-density　surface　electromyography　(sEMG)　sensors　are　susceptible　to　signal　quality　degradation　and　source　variability.　This　study　addresses　the　critical　challenge　of　reduced　gesture　recognition　accuracy　in　armband-based　sEMG　systems　caused　by　concurrent　interference　of　electrode　shift　and　damage.　We　propose　a　hybrid　approach　integrating　a　convolutional　neural　network　(CNN),　a　squeeze-and-excitation　(SE)　attention　block,　and　transfer　learning　(TL).　Data　from　seven　hand　gestures　performed　by　nine　subjects　under　electrode　shift/damage　were　analyzed.　The　SE-CNN　TL　model　achieved　accuracies　of　96.32　±　1.29%　(shift　only),　94.98　±　3.82%　(damage　only),　and　94.30　±　1.51%　(concurrent　interference)—significantly　outperforming　conventional　and　deep　learning　benchmarks.　Notably,　the　accuracy　under　concurrent　interference　represents　the　highest　level　reported　to　date.　This　method　demonstrates　universality　against　diverse　interferences　and　establishes　a　new　state-of-the-art　for　concurrent　interference　mitigation　in　low-density　sEMG　systems.　Our　framework　provides　a　generalized　solution　for　robustness　enhancement　in　sEMG-based　pattern　recognition.　©　2025　Elsevier　Ltd